With AI innovation moving so quickly, it can feel difficult to stay on top of everything that’s happening, including the projects and collaborations, the successes and failures, and “a-ha” breakthrough moments
Across Microsoft, our researchers, engineers, and product teams are building new models, agent frameworks, benchmarks, applications, and research projects at a steady pace. But even when innovative work exists, it has limited impact if people can’t find it, understand it, or put it to use.
That challenge inspired the transformation of our Microsoft Foundry Labs website.
Originally launched as a simple online catalog of AI innovations across Microsoft, Foundry Labs gave teams a place to showcase their research projects, models, and experiments.
But as the volume of innovations increased, the experience became difficult to navigate.
In response, principal product manager Saumil Shrivastava brought together a small cross-functional team of three—a product manager, a marketer, and an engineer—to rebuild the site. Along the way, they discovered something: Modern AI tools could fundamentally change how digital products are designed, prototyped, and delivered.
The team used AI throughout the development process to move from concept to production in approximately three months. The result was a new Foundry Labs experience that helps customers discover innovations across Microsoft while serving as an example of AI-assisted product development in practice.
“The whole purpose of Foundry Labs is to bridge the gap between Microsoft’s latest AI innovations and the people building with them, turning breakthroughs into tools you can explore, test, and build with today.”
Gulsimo Osimi, technical program manager, Microsoft Foundry
Solving the discoverability problem
The mission of Foundry Labs has remained consistent since its creation: Provide a destination where our teams can showcase AI innovations and help people learn about emerging technologies. These innovations come from organizations including Microsoft Research, Microsoft AI, Microsoft Planetary Computer, and other Microsoft teams and collaborators.
But the original site had been designed when the catalog was much smaller.
“When we had 20 innovations, it was pretty reasonable to scroll and click on each innovation,” says Gulsimo Osimi, a technical program manager at Microsoft and member of the three-person site build team, alongside Pratyansh Agrawal, a software engineer, and Patrick Widjaja, a senior product marketing manager. “As the number of projects grew, we wanted the user experience to provide the option to filter by innovation, domains, status, capability, or simply browse with images that help visualize what the innovation is about.”
The challenge was compounded by the pace of AI innovation itself.
“So many teams have lots of AI innovation happening, and staying on top of it is really hard,” Osimi says. “The whole purpose of Foundry Labs is to bridge the gap between Microsoft’s latest AI innovations and the people building with them, turning breakthroughs and experiments into tools you can explore, test, and build with today.”
From traditional specs to a working prototype
The project began with a traditional product management process. Shrivastava set the broader Foundry Labs product vision and strategy, and Osimi translated that direction into a detailed, 32-page specification for the new site experience. But as the document grew, the team realized it wasn’t the ideal way to communicate the vision. Osimi began experimenting with AI-assisted prototyping.
Using AI development tools, Osimi rapidly transformed product concepts into interactive experiences. The process became highly iterative: Specification informed prototype development, and the prototype informed revisions to the specs.
The resulting prototype helped stakeholders understand the vision much more quickly than a document alone.
As the prototype gained visibility, the team presented it at a CoreAI demo fair, where it attracted attention from stakeholders and leadership. That validation helped secure support from engineering and marketing partners, accelerating the site rebuild.
“We’re able to take the project from a sketch to a prototype and then to a full website in only a month.”
Patrick Widjaja, senior product marketing manager, Microsoft Foundry
Three people, multiple disciplines, supported by AI
A key aspect of the project was its deliberately small team. The initiative brought together expertise from product, marketing, and engineering, with AI tools helping each discipline contribute in new ways.
Shrivastava created the broader strategy and brought together the cross-functional team. Osimi led detailed product requirements, user experience design, and AI-assisted prototyping; Widjaja focused on brand identity, community experiences, content strategy, and site storytelling; and Agrawal transformed the prototype into a production-ready experience.
“We used AI extensively in different ways,” Widjaja says. “Gulsimo, from the product side, used it to prototype the website, and I used it to make all of the branding, design, copy, and images. We were able to take the project from a sketch to a prototype and then to a full website in only a month.”
The team’s approach highlighted how AI could support different stages of the development lifecycle.
Building an AI-assisted development workflow
The development process followed a repeatable pattern: receive intent, inspect the codebase, plan changes, create isolated branches, implement functionality, validate outcomes, and merge approved work.
“Microsoft is producing extraordinary AI innovation across research and product teams, but breakthroughs create value only when customers can discover, understand, and put them to use. Our strategy for Foundry Labs is to create a shared path from emerging innovation to real-world impact—giving teams a way to bring promising work forward, learn from customer engagement, and use those insights to accelerate the journey from research into practical use.”
Saumil Shrivastava, principal product manager, Microsoft Foundry
AI-assisted workflows were used to help deliver features across the site, including:
- Interactive innovation catalogs
- Storytelling and community experiences
- Taxonomy-based discovery and filtering
- Searchable innovation pages
- Responsive user experiences
- Performance and accessibility improvements
The engineering team also developed a structured WordPress content model, creating custom content types, taxonomies, field groups, and content management workflows designed to keep the growing catalog maintainable over time.
This focus on content architecture proved especially important because Foundry Labs was never intended to be a static destination. The catalog continues to grow as more innovations are added across Microsoft.
“Microsoft is producing extraordinary AI innovation across research and product teams, but breakthroughs create value only when customers can discover, understand, and put them to use,” Shrivastava says. “Our strategy for Foundry Labs is to create a shared path from emerging innovation to real-world impact—giving teams a way to bring promising work forward, learn from customer engagement, and use those insights to accelerate the journey from research into practical use.”
Designing for discovery
The new experience places discoverability at the center of the user journey.
Visitors can browse innovations by domain, capability, and innovation type. Innovation pages provide concise descriptions, technical details, links to research papers, GitHub repositories, deployment experiences, and related resources.
The site supports a broad range of innovations, including models, SDKs, agent systems, benchmarks, applications, and research projects. Those innovations span disciplines ranging from biomedical research and chemistry to earth science, geospatial intelligence, and general-purpose AI models.
For users, the goal is simple: Arrive with an area of interest and leave with a clear next step.
A central lesson from the site build is that successful AI-assisted development still depends on clear direction and continuous feedback. During prototyping, Osimi found that starting with broad concepts and iterating produced better outcomes than attempting to define every detail up-front.
“I found that having a general idea of what I want and then going back and forth between refining the specification and building the UI has been really effective,” Osimi says.
The team also relied heavily on stakeholder conversations and user feedback throughout development. Those interactions helped clarify assumptions, identify gaps, and refine the final experience.
Human expertise still leads the way
Although AI accelerated many aspects of development, the team emphasized that human expertise remained critical.
“Developing a prototype is one thing, but making it a product is different. You have to ensure performance, security, architecture, and privacy considerations.”
Gulsimo Osimi, technical program manager, Microsoft Foundry
Prototypes can be generated quickly, but production systems require additional considerations, including architecture, scalability, security, privacy, accessibility, and performance.
“Developing a prototype is one thing, but making it a product is different,” Osimi says. “You have to ensure performance, security, architecture, and privacy considerations.”
That collaboration between product, marketing, and engineering helped ensure that Foundry Labs delivered a polished experience rather than just a working prototype.
The result is a platform that continues to evolve as new innovations are added and new community experiences emerge.
Key takeaways
Organizations exploring AI-assisted product development can learn from the Foundry Labs experience:
- Start with a clear vision. Then rapidly iterate between specification and prototype.
- Consider that AI will be used differently across disciplines. How AI factors into each product, marketing, and engineering project will vary, and it’s important that teams have the flexibility to find the right fit.
- Focus on discoverability to support AI adoption. It can be hard to keep track of and learn from AI innovation without the proper platform for people to discover it.
- Validate ideas early. Working prototypes can help generate stakeholder feedback earlier and faster.
- Build for an ever-growing content database. Use structured content models that can scale as the volume of content grows.
